RESUMO
Background: Musculoskeletal injuries (MSKIs) are endemic in military populations. Thus, it is essential to identify and mitigate MSKI risks. Time-to-event machine learning models utilizing self-reported questionnaires or existing data (e.g., electronic health records) may aid in creating efficient risk screening tools. Methods: A total of 4,222 U.S. Army Service members completed a self-report MSKI risk screen as part of their unit's standard in-processing. Additionally, participants' MSKI and demographic data were abstracted from electronic health record data. Survival machine learning models (Cox proportional hazard regression (COX), COX with splines, conditional inference trees, and random forest) were deployed to develop a predictive model on the training data (75%; n = 2,963) for MSKI risk over varying time horizons (30, 90, 180, and 365 days) and were evaluated on the testing data (25%; n = 987). Probability of predicted risk (0.00-1.00) from the final model stratified Service members into quartiles based on MSKI risk. Results: The COX model demonstrated the best model performance over the time horizons. The time-dependent area under the curve ranged from 0.73 to 0.70 at 30 and 180 days. The index prediction accuracy (IPA) was 12% better at 180 days than the IPA of the null model (0 variables). Within the COX model, "other" race, more self-reported pain items during the movement screens, female gender, and prior MSKI demonstrated the largest hazard ratios. When predicted probability was binned into quartiles, at 180 days, the highest risk bin had an MSKI incidence rate of 2,130.82 ± 171.15 per 1,000 person-years and incidence rate ratio of 4.74 (95% confidence interval: 3.44, 6.54) compared to the lowest risk bin. Conclusion: Self-reported questionnaires and existing data can be used to create a machine learning algorithm to identify Service members' MSKI risk profiles. Further research should develop more granular Service member-specific MSKI screening tools and create MSKI risk mitigation strategies based on these screenings.
RESUMO
CONTEXT: Pain during movement screens is a risk factor for musculoskeletal injury (MSKI). Movement screens often require specialized/clinical expertise and large amounts of time to administer. OBJECTIVE: Evaluate if self-reported pain 1) with movement clearing screens is a risk factor for any MSKI, 2) with movement clearing screens is a risk factor for body region-specific MSKIs, and 3) with a greater number of movement clearing screens progressively increases MSKI risk. DESIGN: Retrospective cohort study. SETTING: Field-based. PARTICIPANTS: Military Service members (n=4,222). MAIN OUTCOME MEASURES: Active-duty Service members self-reported pain during movement clearing screens (Shoulder Clearing, Spinal Extension, Squat-Jump-Land). MSKI data were abstracted up to 180-days post-screening. A Traffic Light Model grouped Service members if they self-reported pain during 0 (Green), 1 (Amber), 2 (Red), or 3 (Black) movement clearing screens. Cox proportional hazards models adjusted for age, gender, body mass index, and prior MSKI determined the relationships between pain during movement clearing screens with any and body region-specific MSKIs. RESULTS: Service members self-reporting pain during the Shoulder Clearing (adjusted-Hazard Ratio and 95% confidence interval (HRadj [95%CI]) =1.58 [1.37, 1.82]), Spinal Extension (HRadj=1.48 [1.28, 1.87]), or Squat- Jump-Land (HRadj=2.04 [1.79, 2.32]) tests were more likely to experience any MSKI compared to Service members reporting no pain. Service members with pain during the Shoulder Clearing (HRadj=3.28 [2.57, 4.19]), Spinal Extension (HRadj=2.80 [2.26, 3.49]), or Squat-Jump-Land (HRadj=2.07 [1.76, 2.43]) tests were more likely to experience an upper extremity, spine, back, and torso, or lower extremity MSKI, respectively, compared to Service members reporting no pain. The Amber (HRadj=1.69 [1.48, 1.93]), Red (HRadj=2.07 [1.73, 2.48]), and Black (HRadj=2.31 [1.81, 2.95]) cohorts were more likely to experience an MSKI compared to the Green cohort. CONCLUSIONS: Self-report movement clearing screens in combination with a Traffic Light Model provide clinician/non-clinician-friendly, expedient means to identify Service members at MSKI risk.